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The error threshold is the cornerstone to balance the mathematical complexity and simulation speed of wind farm (WF) equivalent models, and can promote the standardization process of equivalent methodology. Due to differences in power system conditions and model evaluation standards in different countries, the form and indexes of error thresholds of WF equivalent models have not been unified yet. This paper proposes a theoretical method for quantifying the minimum risk of error threshold of WF equivalent models based on the Bayes discriminant criterion. Firstly, the Euclidean errors of WF equivalent models in different periods are quantified, and the probability density distributions of the errors are fitted by kernel density estimation. Secondly, the real-time weighted prior probability algorithm is used to obtain the prior probability of a valid WF equivalent model, and the different losses caused by the missed judgment and misjudgment of the model validity to power systems are taken into account. Thirdly, the minimum risk quantification model of error threshold is established based on the Bayes discriminant criterion, and the feasibility of the proposed method is verified by an actual WF with numerical examples. Compared with the existing error thresholds, the proposed error threshold can more quickly and accurately determine the validity of WF equivalent models. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a measure is developed to evaluate the similarity between the extracted PDFs. Finally, an algorithm is established for determining prior probabilities using fuzzy clustering techniques. By combining these improvements, we develop a more efficient algorithm for classifying image data. An image is assigned to a specific group if it has the highest value of prior probability and a similar level to that group. We explain the proposed algorithm step-by-step with a numerical example and clearly demonstrate its convergence. When applied to multiple image datasets, the proposed algorithm has shown stability and efficiency, outperforming many other statistical and machine learning methods. Additionally, we have developed a Matlab procedure to apply the proposed algorithm to real image datasets. These applications demonstrate the potential of research in various fields related to the digital revolution and artificial intelligence. [ABSTRACT FROM AUTHOR]

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method. [ABSTRACT FROM AUTHOR]

Diagnosing type 1 diabetes in adults is difficult since type 2 diabetes is the predominant diabetes type, particularly with an older age of onset (approximately >30 years). Misclassification of type 1 diabetes in adults is therefore common and will impact both individual patient management and the reported features of clinically classified cohorts. In this article, we discuss the challenges associated with correctly identifying adult-onset type 1 diabetes and the implications of these challenges for clinical practice and research. We discuss how many of the reported differences in the characteristics of autoimmune/type 1 diabetes with increasing age of diagnosis are likely explained by the inadvertent study of mixed populations with and without autoimmune aetiology diabetes. We show that when type 1 diabetes is defined by high-specificity methods, clinical presentation, islet-autoantibody positivity, genetic predisposition and progression of C-peptide loss remain broadly similar and severe at all ages and are unaffected by onset age within adults. Recent clinical guidance recommends routine islet-autoantibody testing when type 1 diabetes is clinically suspected or in the context of rapid progression to insulin therapy after a diagnosis of type 2 diabetes. In this moderate or high prior-probability setting, a positive islet-autoantibody test will usually confirm autoimmune aetiology (type 1 diabetes). We argue that islet-autoantibody testing of those with apparent type 2 diabetes should not be routinely undertaken as, in this low prior-prevalence setting, the positive predictive value of a single-positive islet antibody for autoimmune aetiology diabetes will be modest. When studying diabetes, extremely high-specificity approaches are needed to identify autoimmune diabetes in adults, with the optimal approach depending on the research question. We believe that until these recommendations are widely adopted by researchers, the true phenotype of late-onset type 1 diabetes will remain largely misunderstood. [ABSTRACT FROM AUTHOR]

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate. [ABSTRACT FROM AUTHOR]

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus. [ABSTRACT FROM AUTHOR]

The quality of the input data is amongst the decisive factors affecting the speed and effectiveness of recurrent neural network (RNN) learning. We present here a novel methodology to select optimal training data (those with the highest learning capacity) by approaching the problem from a decision making point of view. The key idea, which underpins the design of the mathematical structure that supports the selection, is to define first a binary relation that gives preference to inputs with higher estimator abilities. The Von Newman Morgenstern theorem (VNM), a cornerstone of decision theory, is then applied to determine the level of efficiency of the training dataset based on the probability of success derived from a purpose-designed framework based on Markov networks. To the best of the author's knowledge, this is the first time that this result has been applied to data selection tasks. Hence, it is shown that Markov Networks, mainly known as generative models, can successfully participate in discriminative tasks when used in conjunction with the VNM theorem. The simplicity of our design allows the selection to be carried out alongside the training. Hence, since learning progresses with only the optimal inputs, the data noise gradually disappears: the result is an improvement in the performance while minimising the likelihood of overfitting. [ABSTRACT FROM AUTHOR]

This paper examines a twofold tension in Gerhard Schurz's (2019) recent proposal to solve Hume's problem of induction. Schurz concedes to the skeptic that there is no non-circular epistemic justification of the reliability of induction, but then argues for the optimality of meta-induction so that if any prediction method is reliable, then meta-induction is. There is a tension in this proposal between meta-induction and our inductive practice: Are we supposed to abandon our inductive practice in favor of meta-induction? Schurz claims that given the actual way the world has been, the meta-inductive method supports the standard inductive method. There then arises a second tension: The challenges Schurz cites against the reliability of induction—such as the anti-inductive hypothesis and the state-uniform probability distribution—cast doubt on Schurz's claim that meta-induction supports induction. The reasoning in this paper moves in the opposite direction: There are ways of answering these challenges in defense of Schurz's claim that meta-induction supports induction, but these ways also point to ways of defending induction directly without the mediation of meta-induction. [ABSTRACT FROM AUTHOR]

This study proposes a new method to classify image data for two groups and effectively apply it in identifying people infected with COVID-19 based on their lung image. First, we extract each image into a two-dimensional interval based on the Gray Level Co-occurrence Matrix (GLCM). Next, find the prior probability based on the fuzzy relationship between the classified element and the established groups. Finally, combining the above improvements, we propose a new principle similar to the Bayesian method for classification. An image is classified into a group if it has the most significant value for prior probability and the smallest value for the overlap distance between the representative interval for the image and the groups. Applying the set of lung X-ray images to distinguish people infected with COVID-19, the proposed algorithm has given outstanding results compared with many well-known methods. The result also shows that this research can be applied in practice, and has the potential for different fields. [ABSTRACT FROM AUTHOR]

In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models. [ABSTRACT FROM AUTHOR]

This project is an illustration of two important concepts in Bayesian Analysis: 1) the prior probability or prior odds, and 2) the posterior probability or posterior odds. We demonstrate that for any probabilistic test result, the actual, or practical, posterior likelihood of deception or truth telling will vary in mathematically predictable ways in response to the prior probability (e.g., base-rate). Results are shown for a distribution of prior odds from 1 in 10 to 9 in 10 using Bayes Factor (i.e., the posterior odds under the equal prior) as a likelihood function to calculate the Bayesian posterior conditional odds of deception or truth-telling. Results show that for any polygraph test score the posterior odds of deception are higher than expected when the prior probability is high, and they are lower than expected when the prior probability is low. [ABSTRACT FROM AUTHOR]

Objective: The present simulation study aimed to assess positive predictive value (PPV) and negative predictive value (NPV) for our newly introduced Accounting for Expected Adjusted Effect test (AEAE test) and compare it to PPV and NPV for a traditional zero-order significance test. Results: The AEAE test exhibited greater PPV compared to a traditional zero-order significance test, especially with a strong true adjusted effect, low prior probability, high degree of confounding, large sample size, high reliability in the measurement of predictor X and outcome Y, and low reliability in the measurement of confounder Z. The zero-order significance test, on the other hand, exhibited higher NPV, except for some combinations of high degree of confounding and large sample size, or low reliability in the measurement of Z and high reliability in the measurement of X/Y, in which case the zero-order significance test can be completely uninformative. Taken together, the findings demonstrate desirable statistical properties for the AEAE test compared to a traditional zero-order significance test. [ABSTRACT FROM AUTHOR]

Background: Low‐back pain (LBP) pathophysiological conditions include nociceptive back pain, somatic referred pain, radicular pain (RP), and radiculopathy. Differential diagnosis is challenging; guidance may come from patients' thorough clinical history and physical examination and, particularly for lumbar RP, from the evaluation of subjective responses of injured lumbar nerves to a strain applied at the buttock (buttock applied strain [BUAS] test). Methods: In a sample of 395 consecutive patients with LBP, sensitivity, specificity, and prior probability (positive predictive values [PPVs] and negative predictive values [NPVs]) of the BUAS test were evaluated against 2 reference tests: the straight leg raising test (SLRT) and the painDETECT (PD) questionnaire. Multinomial logistic regression (MLR) and χ2 analyses were used to evaluate the BUAS test outcomes' dependence upon independent variables (gender, age group, pain localization, SLRT outcomes, and PD outcomes). Cohen's kappa statistic was used to assess inter‐rater agreement. Results: Compared with the PD questionnaire, the BUAS test showed a sensitivity of 92%, specificity of 100%, PPV of 100%, and NPV of 82%; compared with the SLRT, the BUAS test showed a sensitivity of 82%, NPV of 82%, specificity of 40%, and PPV of 40%. Inter‐rater agreement of Cohen's kappa was 0.911. Significant associations were found between BUAS test outcomes and pain localization, SLRT outcomes, and PD outcomes, but not with the predictors gender or age group. MLR showed significant congruent relationships between BUAS test and PD outcomes. Conclusion: Among patients with LBP, the BUAS test showed satisfactory sensitivity, specificity, prior probability, and inter‐rater reliability; thus, it may be considered a useful adjunctive tool to diagnose RP in patients with LBP. For more generalized results, more research, in clinical settings other than pain clinics, is needed. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Presumptive drug testing is commonly used in both the clinical and forensic fields to allow rapid identification of the presence and/or usage of drugs. Because the tests generally have a high sensitivity and specificity (often >90%), then a positive test result may be taken to mean there is a high probability that a targeted drug is present. This assumption is, however, incorrect. This paper demonstrates how, in order to assess the positive predictive value (PPV) of a test, it is necessary to take into account, along with the sensitivity and specificity of the test, the prevalence of the drug in the population being investigated. We demonstrate how an alternative, Bayesian approach to assessing the posterior probability of a drug being present mimics the conventional calculation of PPVs but, because a Bayesian approach requires case‐specific prior probabilities, the posterior probabilities are more meaningful than PPV in any one specific case. The effectiveness of presumptive test results in cases such as drink‐spiking, drug‐driving, testing of drugs during seizures and the confirmation of initial presumptive test results is explored. In order to exploit the potential of presumptive drug testing, it is important that the prevalence of the targeted drugs in relevant populations is understood but, more importantly, it is important to consider using a Bayesian approach in order to tailor results to the specific individual or drug batch being tested. This article is categorized under:Toxicology > Analytical ToxicologyJurisprudence and Regulatory Oversight > Expert Evidence and Narrative [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the greatest challenges to the use of probabilistic reasoning in the assessment of criminal evidence is the 'problem of the prior', i.e. the difficulty in establishing an acceptable prior probability of guilt. Even strong supporters of a Bayesian approach have often preferred to ignore priors and focus on the likelihood ratio (LR) of the evidence. But to calculate if the probability of guilt, given the evidence reaches the probability required for conviction (the standard of proof), the LR has to be combined with a prior. In this article, we propose a solution to the 'problem of the prior': the defendant shall be treated as a member of the set of 'possible perpetrators' defined as the people who had the same or better opportunity as the defendant to commit the crime. For this purpose, we introduce the concept of an 'extended crime scene'. The number of people who had the same or better opportunity as the defendant is the number of people who were just as close or closer to the crime scene, in time and space. We demonstrate how the opportunity prior is incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case. [ABSTRACT FROM AUTHOR]

The integration of sensory with top–down cognitive signals for generating appropriate sensory–motor behaviors is an important issue in understanding the brain's information processes. Recent studies have demonstrated that the interplay between sensory and high-level signals in oculomotor behavior could be explained by Bayesian inference. Specifically, prior knowledge for motion speed introduces a bias in the speed of smooth pursuit eye movements. The other important prediction of Bayesian inference is variability reduction by prior expectation; however, there is insufficient evidence in oculomotor behaviors to support this prediction. In the present study, we trained monkeys to switch the prior expectation about motion direction and independently controlled the strength of the motion stimulus. Under identical sensory stimulus conditions, we tested if prior knowledge about the motion direction reduced the variability of open-loop smooth pursuit eye movements. We observed a significant reduction when the prior expectation was strong; this was consistent with the prediction of Bayesian inference. Taking advantage of the open-loop smooth pursuit, we investigated the temporal dynamics of the effect of the prior to the pursuit direction bias and variability. This analysis demonstrated that the strength of the sensory evidence depended not only on the strength of the sensory stimulus but also on the time required for the pursuit system to form a neural sensory representation. Finally, we demonstrated that the variability and directional bias change by prior knowledge were quantitatively explained by the Bayesian observer model. [ABSTRACT FROM AUTHOR]

Algorithms for magnetic resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this letter, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically variational autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (${N}=8$), and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. [ABSTRACT FROM AUTHOR]

While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham's razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham's razor is applied to nested economic models. I will argue that previous responses to the problem found in the philosophical literature are unsatisfactory and develop a novel reply to the problem. [ABSTRACT FROM AUTHOR]

Many Bayes factors have been proposed for comparing population means in two-sample (independent samples) studies. Recently, Wang and Liu presented an "objective" Bayes factor (BF) as an alternative to a "subjective" one presented by Gönen et al. Their report was evidently intended to show the superiority of their BF based on "undesirable behavior" of the latter. A wonderful aspect of Bayesian models is that they provide an opportunity to "lay all cards on the table." What distinguishes the various BFs in the two-sample problem is the choice of priors (cards) for the model parameters. This article discusses desiderata of BFs that have been proposed, and proposes a new criterion to compare BFs, no matter whether subjectively or objectively determined. A BF may be preferred if it correctly classifies the data as coming from the correct model most often. The criterion is based on a famous result in classification theory to minimize the total probability of misclassification. This criterion is objective, easily verified by simulation, shows clearly the effects (positive or negative) of assuming particular priors, provides new insights into the appropriateness of BFs in general, and provides a new answer to the question, "Which BF is best?" [ABSTRACT FROM AUTHOR]

The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy local double neighborhood information ${\text{c}}$ -means (FLDNICM) clustering algorithm for remotely sensed imagery classification, which incorporates flexible and accurate local spatial and spectral information. First, a trade-off weighted fuzzy factor is established based on a pixel spatial attraction model that considers spatial distance and class membership differences between the central pixel and its neighbor simultaneously. This factor can adaptively and accurately estimate the spatial constraints from neighboring pixels. To further enhance robustness to noise and outliers, another fuzzy prior probability function is also defined, which integrates the mutual dependence information from a pixel and its neighbor in a fuzzy logical way for obtaining accurate spatial contextual information. The FLDNICM enhances the conventional fuzzy ${\text{c}}$ -means algorithm by producing homogeneous segmentation while reducing the edge blurring artifacts. The new trade-off weighted fuzzy factor and prior probability function are both parameter free and fully adaptive to the image content. Experimental results demonstrate the superiority of FLDNICM over competing methodologies, considering a series of synthetic and real-world images classification applications. [ABSTRACT FROM AUTHOR]

Many who take a dismissive attitude towards metaphysics trace their view back to Carnap’s ‘Empiricism, Semantics and Ontology’ (1950a). But the reason Carnap takes a dismissive attitude to metaphysics is a matter of controversy. I will argue that no reason is given in ‘Empiricism, Semantics and Ontology’, and this is because his reason for rejecting metaphysical debates was given in ‘Pseudo-Problems in Philosophy’ (1928). The argument there assumes verificationism, but I will argue that his argument survives the rejection of verificationism. The root of his argument is the claim that metaphysical statements cannot be justified; the point is epistemic, not semantic. I will argue that this remains a powerful challenge to metaphysics that has yet to be adequately answered. [ABSTRACT FROM AUTHOR]

The attack graph methodology can be used to identify the potential attack paths that an attack can propagate. A risk assessment model based on Bayesian attack graph is presented in this paper. Firstly, attack graphs are generated by the MULVAL (Multi-host, Multistage Vulnerability Analysis) tool according to sufficient information of vulnerabilities, network configurations and host connectivity on networks. Secondly, the probabilistic attack graph is established according to the causal relationships among sophisticated multi-stage attacks by using Bayesian Networks. The probability of successful exploits is calculated by combining index of the Common Vulnerability Scoring System, and the static security risk is assessed by applying local conditional probability distribution tables of the attribute nodes. Finally, the overall security risk in a small network scenario is assessed. Experimental results demonstrate our work can deduce attack intention and potential attack paths effectively, and provide effective guidance on how to choose the optimal security hardening strategy. [ABSTRACT FROM AUTHOR]

A Bayesian assessment of the probability that the defendant in a criminal trial is guilty depends on the presumed base rate for guilt and the estimated likelihoods of the evidence. This article explores how the base rate shall be determined. Bayesian scholars have recommended a base rate of 1/N, where N is the number of 'possible perpetrators', but it is unclear how the reference class of possible perpetrators shall be defined. Several solutions are explored, and it is demonstrated that each solution leads to serious sacrifices in some fundamental principle of criminal justice. Some solutions lead to arbitrary assessments, or assessments that deviate from the facts. Other solutions fail to uphold an acceptable ratio between wrongful acquittals and wrongful convictions. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity. [ABSTRACT FROM AUTHOR]

In this paper, an efficient Bayesian-Emulator approach is developed and applied to identify unknown input parameters for detailed energy models for existing buildings. The new approach improves on the traditional Bayesian approach for parameter identification, characterized by high computing requirements. Specifically, the developed approach utilizes a building energy model emulator to expedite the parameter identification process. Both discrete and continuous building energy model parameters can be identified using the developed Bayesian-Emulator approach. In the paper, the benefits of the developed Bayesian-Emulator parameter identification approach to calibrate whole-building energy models are discussed. Moreover, the proposed approach has been applied to a case study of an existing medium office building using monthly utility data. It is found that the Bayesian-Emulator is capable to calibrate the medium office building energy model in less than 1 minute using a personal computer. The Bayesian-Emulator approach presented in this paper can be an effective framework to automatically calibrate energy models for existing buildings. [ABSTRACT FROM AUTHOR]

In this paper, we study the role of prior probability on the efficiency of quantum local adiabatic search algorithm. The following aspects for prior probability are found here: firstly, only the probabilities of marked states affect the running time of the adiabatic evolution; secondly, the prior probability can be used for improving the efficiency of the adiabatic algorithm; thirdly, like the usual quantum adiabatic evolution, the running time for the case of multiple solution states where the number of marked elements are smaller enough than the size of the set assigned that contains them can be significantly bigger than that of the case where the assigned set only contains all the marked states. [ABSTRACT FROM AUTHOR]

According to an emerging view, decision-making, and motor planning are tightly entangled at the level of neural processing. Choice is influenced not only by the values associated with different options, but also biased by other factors. Here we test the hypothesis that preliminary action planning can induce choice biases gradually and independently of objective value when planning overlaps with one of the potential action alternatives. Subjects performed center-out reaches obeying either a clockwise or counterclockwise cue-response rule in two tasks. In the probabilistic task, a pre-cue indicated the probability of each of the two potential rules to become valid. When the subsequent rule-cue unambiguously indicated which of the pre-cued rules was actually valid (instructed trials), subjects responded faster to rules pre-cued with higher probability. When subjects were allowed to choose freely between two equally rewarded rules (choice trials) they chose the originally more likely rule more often and faster, despite the lack of an objective advantage in selecting this target. In the amount task, the pre-cue indicated the amount of potential reward associated with each rule. Subjects responded faster to rules pre-cued with higher reward amount in instructed trials of the amount task, equivalent to the more likely rule in the probabilistic task. Yet, in contrast, subjects showed hardly any choice bias and no increase in response speed in favor of the original high-reward target in the choice trials of the amount task. We conclude that free-choice behavior is robustly biased when predictability encourages the planning of one of the potential responses, while prior reward expectations without action planning do not induce such strong bias. Our results provide behavioral evidence for distinct contributions of expected value and action planning in decision-making and a tight interdependence of motor planning and action selection, supporting the idea that the underlying neural mechanisms overlap. [ABSTRACT FROM AUTHOR]

Although we are able to rapidly understand novel scene images, little is known about the mechanisms that support this ability. Theories of optimal coding assert that prior visual experience can be used to ease the computational burden of visual processing. A consequence of this idea is that more probable visual inputs should be facilitated relative to more unlikely stimuli. In three experiments, we compared the perceptions of highly improbable real-world scenes (e.g., an underwater press conference) with common images matched for visual and semantic features. Although the two groups of images could not be distinguished by their low-level visual features, we found profound deficits related to the improbable images: Observers wrote poorer descriptions of these images (Exp. 1), had difficulties classifying the images as unusual (Exp. 2), and even had lower sensitivity to detect these images in noise than to detect their more probable counterparts (Exp. 3). Taken together, these results place a limit on our abilities for rapid scene perception and suggest that perception is facilitated by prior visual experience. [ABSTRACT FROM AUTHOR]

We revisit the classic thermodynamic problem of maximum work extraction from arbitrary-sized source of heat and sink, modelled as perfect gases. For a given initial state of the process, we assume ignorance of the final temperatures. We quantify the prior information about the process and assign a prior distribution to the unknown temperature(s). This requires that we also take into account the temperature values which are not regarded in standard analysis. In the present formulation, however, such values appear to be consistent with the given prior information and hence are included here in the inference. We derive estimates of the efficiency at optimal work from the expected values of the final temperatures, and show that these match with the exact expressions in the limit when any one of the systems is very large compared to the other. For other relative sizes of the source and the sink, a weighted mean is defined over the estimates from two valid inference procedures, that generalizes the procedure suggested earlier in. The mean estimate for efficiency obtained in this way agrees with the results of the optimal performance quite accurately. [ABSTRACT FROM AUTHOR]

ABSTRACT Age-related anatomical changes to the surface of the pubic symphysis are well-documented in the literature. However, aligning these morphological changes with chronological age has proven problematic, often resulting in biased age estimates. Statistical modeling provides an avenue for forensic anthropologists and bioarchaeologists to increase the accuracy of traditional aging methods. Locating appropriate samples to use as a basis for modeling age estimations can be challenging due to differing sample age distributions and potentially varying patterns of senescence. We compared two approaches, Rostock and Forensic, coupled with a Bayesian methodology, to address these issues. Transition analysis was run specific to each method (which differ by sample selection). A Gompertz model was derived from an informative prior that yielded the mortality and senescence parameters for constructing highest posterior density ranges, i.e., coverages, which are analogous to age ranges. These age ranges were generated from both approaches and are presented as reference tables useful for historic male and female Italian samples. The age ranges produced from each approach were tested on an historic Italian sample, using cumulative binomial tests. These two approaches performed similarly, with the Forensic approach showing a slight advantage. However, the Forensic approach is unable to identify varying senescence patterns between populations, thus preference for one approach over the other will depend on research design. Finally, we demonstrate that while populations exhibit similar morphological changes with advancing age, there are no significant sex differences in these samples, and the timing of these changes varies from population to population. Am J Phys Anthropol 156:466-473, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Fuzzy c‐means (FCMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well‐known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre‐defined parameters that are used in traditional FCM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. Compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state‐of‐the‐art technologies including extensions of possibilistic fuzzy c‐means (PFCM), GMM, FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data are often affected by speckle noise, which originates in the SAR system's coherent nature. In this paper, we introduce a simple and effective algorithm to make the traditional Student's t-mixture model (SMM) more robust to noise. The proposed new modified SMM (MSMM) is applied for SAR image segmentation. SMM has come to be regarded as an alternative to the Gaussian mixture model (GMM) as it is heavy tailed and more robust to outliers. However, a major shortcoming of this method is that it does not take into account the spatial dependencies in the image. Although some existing methods incorporate the spatial relationship between neighboring pixels, they are still not robust enough to noise. The advantages of our method are as follows. First, we introduce MSMM to incorporate the local spatial information and pixel intensity value by considering the conditional probability of an image pixel influenced by the probabilities of pixels in its immediate neighborhood. Furthermore, we introduce the additional parameter α to control the extent of this influence. The larger α indicates the heavier extent of influence in the neighborhoods. Second, the prior probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood, which incorporates local spatial and component information. Third, our model is based on the finite mixture model (FMM); it is simple and easy to implement, and the expectation maximization algorithm can be applied for estimation of optimal parameters. Finally, the traditional SMM can be considered as a special case of our model. Thus, our method is general enough for FMM-based techniques. Experimental results on both simulated and real SAR images demonstrate the improved robustness and effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

The idea that perceptual and cognitive systems must incorporate knowledge about the structure of the environment has become a central dogma of cognitive theory. In a Bayesian context, this idea is often realized in terms of 'tuning the prior'-widely assumed to mean adjusting prior probabilities so that they match the frequencies of events in the world. This kind of 'ecological' tuning has often been held up as an ideal of inference, in fact defining an 'ideal observer.' But widespread as this viewpoint is, it directly contradicts Bayesian philosophy of probability, which views probabilities as degrees of belief rather than relative frequencies, and explicitly denies that they are objective characteristics of the world. Moreover, tuning the prior to observed environmental frequencies is subject to overfitting, meaning in this context overtuning to the environment, which leads (ironically) to poor performance in future encounters with the same environment. Whenever there is uncertainty about the environment-which there almost always is-an agent's prior should be biased away from ecological relative frequencies and toward simpler and more entropic priors. [ABSTRACT FROM AUTHOR]

Humans use prior knowledge to bias decisions made under uncertainty. In this fMRI study we predicted that different brain dynamics play a role when prior knowledge is added to decisions made under perceptual vs. categorical uncertainty. Subjects decided whether shapes belonged to Category S – smoother – or Category B – bumpier – under both uncertainty conditions, with or without prior knowledge cues.When present, the prior knowledge cue, 80/20 or 50/50, indicated that 80 and 20% (or 50 and 50%) were the chances that responding “S” and “B” (or vice versa) would be correct. During perceptual uncertainty, shapes were degraded with noise. During categorical uncertainty, shapes were ambiguous. Adding the 80/20 cue increased activation during perceptual uncertainty in bilateral lateral occipital (LO) cortex and left middle frontal gyrus (MidFG), and decreased activity in bilateral LO cortex during categorical uncertainty. Right MidFG and other frontoparietal regions were active in all conditions. The results demonstrate that left MidFG shows activation changes, suggestive of an influence on visual cortex, that depend on the factor that makes the decisions difficult. When sensory evidence is difficult to perceive, prior knowledge increases visual cortical activity. When the sensory evidence is easy to perceive but difficult to interpret, prior knowledge decreases visual cortical activity. [ABSTRACT FROM AUTHOR]

In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user's locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses. [ABSTRACT FROM PUBLISHER]

In many signal detection and classification problems, we have knowledge of the distribution under each hypothesis, but not the prior probabilities. This paper is aimed at providing theory to quantify the performance of detection via estimating prior probabilities from either labeled or unlabeled training data. The error or risk is considered as a function of the prior probabilities. We show that the risk function is locally Lipschitz in the vicinity of the true prior probabilities, and the error of detectors based on estimated prior probabilities depends on the behavior of the risk function in this locality. In general, we show that the error of detectors based on the maximum likelihood estimate (MLE) of the prior probabilities converges to the Bayes error at a rate of n^-1/2, where n is the number of training data. If the behavior of the risk function is more favorable, then detectors based on the MLE have errors converging to the corresponding Bayes errors at optimal rates of the form n^-(1+\alpha)/2, where \alpha > 0 is a parameter governing the behavior of the risk function with a typical value \alpha = 1. The limit \alpha \rightarrow \infty corresponds to a situation where the risk function is flat near the true probabilities, and thus insensitive to small errors in the MLE; in this case, the error of the detector based on the MLE converges to the Bayes error exponentially fast with n. We show that the bounds are achievable no matter given labeled or unlabeled training data and are minimax-optimal in the labeled case. [ABSTRACT FROM AUTHOR]

Introduction. This is an international study of the epidemiology of family medicine (FM) in three practice populations from the Netherlands, Malta and Serbia. Diagnostic associations between common reasons for encounter (RfEs) and episodes titles are compared and similarities and differences are described and analysed. Methodology. Participating family doctors (FDs) recorded details of all their patient contacts in an ‘episode of care (EoC)’ structure using the International Classification of Primary Care (ICPC). RfEs presented by the patient and episode titles (diagnostic labels of EoCs) were classified with ICPC. The relationships between RfEs and episode titles were studied with Bayesian methods. Results. Distributions of diagnostic odds ratios (ORs) from the three population databases are presented and compared. Conclusions. ICPC, the RfE and the EoC data model are appropriate tools to study the process of diagnosis in FM. Distributions of diagnostic associations between RfEs and episode titles in the Transition Project international populations show remarkable similarities and congruencies in the process of diagnosis from both the RfE and the episode title perspectives. The congruence of diagnostic associations between populations supports the use of such data from one population to inform diagnostic decisions in another. Differences in the magnitude of such diagnostic associations are significant, and population-specific data are therefore desirable. We propose that both an international (common) and a local (health care system specific) content of FM exist and that the empirical distributions of diagnostic associations presented in this paper are a reflection of both these effects. We also observed that the frequency of exposure to such diagnostic challenges had a strong effect on the confidence intervals of diagnostic ORs reflecting these diagnostic associations. We propose that this constitutes evidence that expertise in FM is associated with frequency of exposure to diagnostic challenges. [ABSTRACT FROM PUBLISHER]

Background This is a study of the relationships between common reasons for encounter (RfEs) and common diagnoses (episode titles) within episodes of care (EoCs) in family practice populations in four countries. Method Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the International Classification of Primary Care (ICPC), including RfEs presented by the patient, and the FDs' diagnostic labels. The relationships between RfEs and episode titles were studied using Bayesian methods. Results The RfE 'cough' is a strong, reliable predictor for the diagnoses 'cough' (a symptom diagnosis), 'acute bronchitis', 'URTI' and 'acute laryngitis/tracheitis' and a less strong, but reliable predictor for 'sinusitis', 'pneumonia', 'influenza', 'asthma', 'other viral diseases (NOS)', 'whooping cough', 'chronic bronchitis', 'wheezing' and 'phlegm'. The absence of cough is a weak but reliable predictor to exclude a diagnosis of 'cough', 'acute bronchitis' and 'tracheitis'. Its presence allows strong and reliable exclusion of the diagnoses 'gastroenteritis', 'no disease' and 'health promotion/prevention', and less strong exclusion of 'adverse effects of medication'. The RfE 'sadness' is a strong, reliable predictor for the diagnoses 'feeling sad/depressed' and 'depressive disorder'. It is a less strong, but reliable predictor of a diagnosis of 'acute stress reaction'. The absence of sadness (as a symptom) is a weak but reliable predictor to exclude the symptom diagnosis 'feeling sad/depressed'. Its presence does not support the exclusion of any diagnosis. Conclusions We describe clinically and statistically significant diagnostic associations observed between the RfEs 'cough' and 'sadness', presenting as a new problem in family practice, and all the episode titles in ICPC. [ABSTRACT FROM AUTHOR]

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique—Hybrid Bayesian Classifier (HBC)—where the class prior probabilities are determined by unmixing a supplemental low spatial–high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial–low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments—first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered. [ABSTRACT FROM PUBLISHER]

The deterioration of the condition of process plants assets has a major negative impact on the safety of its operation. Risk based integrity modeling provides a methodology to quantify the risks posed by an aging asset. This provides a means for the protection of human life, financial investment and the environmental damage from the consequences of its failures. This methodology is based on modeling the uncertainty in material degradations using probability distributions, known as priors. Using Bayes theorem, one may improve the prior distribution to obtain a posterior distribution using actual inspection data. Although the choice of priors is often subjective, a rational consensus can be achieved by judgmental studies and analyzing the generic data from the same or similar installations. The first part of this paper presents a framework for a risk based integrity modeling. This includes a methodology to select the prior distributions for the various types of corrosion degradation mechanisms, namely, the uniform, localized and erosion corrosion. Several statistical tests were conducted based on the data extracted from the literature to check which of the prior distributions follows data the best. Once the underlying distribution has been confirmed, one can estimate the parameters of the distributions. In the second part, the selected priors are tested and validated using actual plant inspection data obtained from existing assets in operation. It is found that uniform corrosion can be best described using 3P-Weibull and 3P-Lognormal distributions. Localized corrosion can be best described using Type1 extreme value and 3P-Weibull, while erosion corrosion can best be described using the 3P-Weibull, Type1 extreme value, or 3P-Lognormal distributions. [ABSTRACT FROM AUTHOR]